A stable control system is easily designed with the simple adaptive control (SAC) method by using the almost strictly positive real (ASPR) condition of a plant. For the plant that does not satisfy ASPR, an SAC system ...
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Lung cancer has the highest mortality rate, and patients with non-small cell lung cancer (NSCLC) account for 75% to 80% of these cases. Treatment response varies greatly among patients. Therefore, there is significant...
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Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no ...
Semantic segmentation enables high-accuracy object classification by assigning a class label to each pixel in an image. However, creating training data for segmentation is labor-intensive as it involves labeling every...
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ISBN:
(数字)9798350355079
ISBN:
(纸本)9798350355086
Semantic segmentation enables high-accuracy object classification by assigning a class label to each pixel in an image. However, creating training data for segmentation is labor-intensive as it involves labeling every pixel. To alleviate the training cost, computer graphics (CG) datasets are considered as an efficient alternative solution. However, models trained on CG datasets may induce a significant drop in the intersection over union (IoU) when applied to real-world images due to domain differences. In this work, semi-supervised domain adaptation (SSDA) and unsupervised domain adaptation (UDA) training methods are adopted to achieve efficient segmentation models for intersection images. SSDA utilizes a small amount of labeled data from the target domain, while UDA does not use any labeled data from the target domain. Simulation results show that SSDA achieves similar accuracy to supervised learning across most classes, and UDA achieves comparable accuracy to supervised learning in certain classes.
This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject...
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This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject to multiple static,structured stochastic uncertainties,and seek to derive fundamental conditions to ensure that a system can be stabilized under a mean-square *** the stochastic control framework,this problem can be considered as one of optimal control under state-or input-dependent random noises,while in the networked control setting,a problem of networked feedback stabilization over lossy communication *** authors adopt a mean-square small gain analysis approach,and obtain necessary and sufficient conditions for a system to be meansquare stabilizable via output *** single-input,single-output(SISO)systems,the condition provides an analytical bound,demonstrating explicitly how plant unstable poles,nonminimum phase zeros,and time delay may impose a limit on the uncertainty variance required for mean-square *** MIMO minimum phase systems with possible delays,the condition amounts to solving a generalized eigenvalue problem,readily solvable using linear matrix inequality optimization techniques.
In this work, a video communication system to encode river video at a low bit rate is proposed. In the proposed system, a semantic segmantation is performed to organize the coding priority of each segment. Then, by se...
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ISBN:
(数字)9798350379051
ISBN:
(纸本)9798350379068
In this work, a video communication system to encode river video at a low bit rate is proposed. In the proposed system, a semantic segmantation is performed to organize the coding priority of each segment. Then, by setting an appropriate Quantization Parameter (QP) map for each region with VVenC which is the latest video codec of VVC, a low bitrate coding is achieved. We also compare the encoding performance with the latest Neural Video Codec (NVC), namely, DCVC-DC. The simulation results show that the proposed method with VVenC achieves ultra-low bit coding. Although the current version of DCVC-DC could not achieve ultra-low bit coding, the coding efficiency shows higher performance than that using VVenC in a wide range of bit rate.
With the spread of high-resolution video such as 8K and 360-degree videos, efficient video coding methods is still highly required. The intra prediction method of versatile video coding (VVC) achieves efficient coding...
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ISBN:
(数字)9798350379051
ISBN:
(纸本)9798350379068
With the spread of high-resolution video such as 8K and 360-degree videos, efficient video coding methods is still highly required. The intra prediction method of versatile video coding (VVC) achieves efficient coding by referencing neighboring pixel values based on predefined directional modes. However, it is difficult to further improve video coding efficiency using traditional linear models in the intra prediction mode. In this work, we propose a method to increase the coding efficiency by applying the image restoration technique. Specifically, image restoration technique uses Conditional-UNet combined with a stochastic differential equation (SDE) to generate highly accurate predicted pixels. The simulation results show that the proposed algorithm can achieve an improvement of 29.14 % on BD-rate compared to the original VVC algorithm at random access. Moreover, the proposed algorithm reduces the training and inference time through optimized parameters.
In recent applications, a modern object recognition model is available together with the video encoder. In this work, an adaptive bitrate control algorithm is proposed using a You Only Look Once v8 (YOLOv8) model for ...
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ISBN:
(数字)9798350355079
ISBN:
(纸本)9798350355086
In recent applications, a modern object recognition model is available together with the video encoder. In this work, an adaptive bitrate control algorithm is proposed using a You Only Look Once v8 (YOLOv8) model for Versatile Video Coding (VVC). YOLOv8 object detection network, which is also suitable for real-time processing, is used as an algorithm to detect salient regions before encoding. The proposed algorithm estimates Rate-Distortion Cost (RD-Cost) of Coding Tree Units (CTUs) with YOLOv8 and selects the Quantization Parameter (QP) based on the object detection result. Simulation results show that the proposed algorithm achieves a maximum bitrate reduction rate of 7.76% and improves coding efficiency while minimizing image quality degradation.
Unmanned aerial vehicles (UAVs) have remarkably advanced and expected to be useful in a variety of fields. However, there is a serious problem of video quality degradation due to rainy weather, regardless of whether t...
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ISBN:
(数字)9798350379051
ISBN:
(纸本)9798350379068
Unmanned aerial vehicles (UAVs) have remarkably advanced and expected to be useful in a variety of fields. However, there is a serious problem of video quality degradation due to rainy weather, regardless of whether the UAVs are flying autonomously. In this work, we propose a highly accurate method to remove the effect of rain streaks from images captured by UAVs in rainy conditions. This approach uses the multi-axis feature fusion (MFF) block including the kernel basis attention (KBA) module. The proposed method shows better performance than the previous deraining methods in PSNR and SSIM. Moreover, the proposed method is effective for varying rain streak intensity.
In recent years, efficient mobility is required for omni-directional mobile robots in the logistics industry. There are various types of rollers mounted on mobile robots. Among them, omni-directional rollers (omni rol...
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